Iris recognition is an emerging noninvasive biometric technology. The iris is very
suitable for the verification and the identification of humans due to its distinctive and
stable spatial patterns. In this paper, we propose an effective iris recognition algorithm
which adopts a bank of Gabor filters combined with the estimated fractal dimension. After
the preprocessing procedure, the normalized effective iris region is decomposed according
to different frequency regions by the multi-channel Gabor filters. The texture information
of the filtered images is obtained via the differential box-counting method. A
feature selection scheme is then adopted to remove the unimportant features to reduce the
amount of data and improve the performance. The experimental results on the CASIA
database show that the proposed method has a very high recognition rate.